(1) Field of Invention
The present invention relates to a system for ground-plane homography estimation and, more particularly, to a system for ground-plane homography estimation using adaptive feature selection.
(2) Description of Related Art
In the field of computer vision and computer graphics, any two images of the same planar surface in space are related by a homography (assuming a pinhole camera model). Homography is a concept in the mathematical science of geometry. A homography is an invertible transformation from a projective space (e.g., the real projective plane) to itself that maps straight lines to straight lines. Through homography mapping, the image coordinate in image A of a physical planar surface point can be mapped to the coordinate in image B of the same point. This has many practical applications, such as image rectification, image registration, or computation of camera motion (rotation and translation) between two images. Once camera rotation and translation have been extracted from an estimated homography matrix, this information may be used for navigation, or to insert models of three-dimensional (3D) objects into an image or video, so that they are rendered with the correct perspective and appear to have been part of the original scene, as in augmented reality.
Conventional homography estimation methods in the prior art use random sample consensus (RANSAC) to find the homography function between two images of the same scene, such as two consecutive images taken by an airborne platform looking down to the wound. RANSAC picks random samples of the given correspondences between them, fits the homography function, and then evaluates the function to all possible correspondences. Evaluation of the fitted homography is a process to find a function that has a maximum number of inliers among all the correspondences. Thus, the conventional ground-plane homography estimation methods assume that the correspondences lie on the planar surface (i.e., the ground plane). If a significant amount of the feature correspondences is from either moving or above-ground objects, the estimated homography may contains errors and inaccuracies, since those outlier correspondences do not hold the ground-homography transform.
The moving object detection and tracking algorithm described by Bhattacharya et al. in “Moving Object Detection and Tracking in Infra-red Aerial Imagery,” Machine Vision Beyond Visible Specturm, Augmented Vision and Reality, Volume 1, 2011 (which is hereby incorporated by reference as though fully set forth herein) performs pound-plane homography estimation in forward looking infrared aerial imagery. It uses speeded up robust features (SURF) and Kanade-Lucas-Tomasi (KLT) features to regulate cumulative homography drift, but the algorithm still has the problem of non-planar outlier features.
The locally optimized RANSAC approach described by Chum et al. in Locally Optimized RANSAC,” Pattern Recognition (2003), pp. 236-243 (which is hereby incorporated by reference as though fully set forth herein) modified RANSAC so that it simultaneously improves the speed of the algorithm and the quality of the solution by introduction of two local optimization methods, but it didn't actively exclude outlier features for quality improvement.
Each of the prior methods described above exhibit limitations that make them incomplete. Thus, a continuing need exists for a method for robust ground-plane homography estimation using adaptive feature selection, which includes feature exclusion from moving or above-ground objects and a sub-region feature correspondence limitation